class_docstr:     InfoGraph model for unsupervised setting
  
  Parameters
  -----------
  in_dim: int
      Input feature size.
  hid_dim: int
      Hidden feature size.
  n_layer: int
      Number of the GNN encoder layers.
  
  Functions
  -----------
  forward(graph):
      graph: DGLGraph
code: def get_embedding(self, graph, feat):
      # get_embedding function for evaluation the learned embeddings
  
      with th.no_grad():
          global_emb, _ = self.encoder(graph, feat)
  
      return global_emb
  
imports: import torch
  import torch.nn
  import torch.nn.functional
  from dgl.nn import GINConv, NNConv, Set2Set
  from dgl.nn.pytorch.glob import SumPooling
  from torch.nn import BatchNorm1d, GRU, Linear, ModuleList, ReLU, Sequential
  from utils import global_global_loss_, local_global_loss_
label_desc: Use image node_id 2 for calling the InfoGraph obj's underlying member method code with example usage: obj.get_embedding(graph, feat) and returns: global_emb